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基于小波分析和卷积神经网络的滚动轴承早期故障告警方法

刘西洋 陈果 尉询楷 刘曜宾 王浩 贺志远

刘西洋, 陈果, 尉询楷, 等. 基于小波分析和卷积神经网络的滚动轴承早期故障告警方法[J]. 航空动力学报, 2024, 39(9):20220622 doi: 10.13224/j.cnki.jasp.20220622
引用本文: 刘西洋, 陈果, 尉询楷, 等. 基于小波分析和卷积神经网络的滚动轴承早期故障告警方法[J]. 航空动力学报, 2024, 39(9):20220622 doi: 10.13224/j.cnki.jasp.20220622
LIU Xiyang, CHEN Guo, WEI Xunkai, et al. Early fault alarm method of rolling bearing based on wavelet analysis and convolution neural network[J]. Journal of Aerospace Power, 2024, 39(9):20220622 doi: 10.13224/j.cnki.jasp.20220622
Citation: LIU Xiyang, CHEN Guo, WEI Xunkai, et al. Early fault alarm method of rolling bearing based on wavelet analysis and convolution neural network[J]. Journal of Aerospace Power, 2024, 39(9):20220622 doi: 10.13224/j.cnki.jasp.20220622

基于小波分析和卷积神经网络的滚动轴承早期故障告警方法

doi: 10.13224/j.cnki.jasp.20220622
基金项目: 国家科技重大专项(J2019-Ⅳ-004-0071); 国家自然科学基金(52272436); 江苏省研究生科研与实践创新计划项目(KYCX20_0211)
详细信息
    作者简介:

    刘西洋(1994-),女,博士生,主要从事航空发动机状态监测与故障诊断技术研究。 E-mail:lxycca@nuaa.edu.cn

    通讯作者:

    陈果(1972-),男,教授、博士生导师,博士,主要从事航空发动机整机振动、状态监测与故障诊断研究。 E-mail:cgzyx@263.net

  • 中图分类号: V263.6

Early fault alarm method of rolling bearing based on wavelet analysis and convolution neural network

  • 摘要:

    针对航空发动机主轴承状态监测中存在的真实故障样本难以获取、变工况通用告警阈值难以界定以及早期微弱故障难以识别问题,提出一种滚动轴承早期故障通用告警方法。该方法仅基于正常样本训练卷积神经网络,依靠退化数据与正常数据间的特征距离来构造演化状态指示器,并基于训练标签实现不同工况数据告警阈值的统一,同时利用小波频带包络信号对早期高频故障的敏感性实现提前预警;然后,基于拉依达准则划分演化阶段,确定退化与失效阈值;最后基于粒子滤波对剩余寿命进行了逐步跟踪预测。3组试验结果证明,基于不同故障试验数据的小波分析和卷积神经网络(Wavelet-CNN)特征,其退化阈值与失效阈值能被归一化在0.6和1.0附近,且对退化开始时间的预测较非小波方法分别提前13.01%、12.33%及13.70%。

     

  • 图 1  滚动轴承故障演化状态指示与剩余寿命预测

    Figure 1.  Fault evolution state indication and residual life prediction of rolling bearings

    图 2  CNN结构

    Figure 2.  Structure of CNN

    图 3  基于CNN的滚动轴承故障演化状态指示器获取流程

    Figure 3.  Acquisition process of rolling bearing fault evolution status indicator based on CNN

    图 4  3组滚动轴承全寿命试验装置

    Figure 4.  Three groups of rolling bearing life test equipments

    图 5  3组滚动轴承最终发生故障类型

    Figure 5.  Final failure types of three groups of rolling bearing

    图 6  3组全寿命数据特征值演化对比

    Figure 6.  Comparison of characteristic value evolution based on three groups of whole lifetime data

    图 7  正常阶段数据训练标签的不同设置下CNN特征提取结果

    Figure 7.  CNN feature extraction results under different settings of training labels in bearing normal stage

    图 8  ZA 2115轴承全寿命阶段通频特征与小波细节特征演化趋势

    Figure 8.  Evolution trend of full band characteristics and wavelet details characteristics of ZA 2115 bearing in whole life stage

    图 9  HRB 6206轴承全寿命阶段通频特征与小波细节特征演化趋势

    Figure 9.  Evolution trend of full band characteristics and wavelet details characteristics of HRB 6206 bearing in whole life stage

    图 10  UER 20轴承全寿命阶段通频特征与小波细节特征演化趋势

    Figure 10.  Evolution trend of full band characteristics and wavelet details characteristics of UER 20 bearing in whole life stage

    图 11  基于粒子滤波的3组滚动轴承数据退化阶段RUL预测结果

    Figure 11.  RUL prediction results of three groups of rolling bearing data in degradation stage based on particle filter

    表  1  CNN参数

    Table  1.   Parameters of CNN

    项目 输入层 池化层 卷积层 池化层 卷积层 池化层 卷积层 全连接层 回归层
    参数 64×32×1 2×2 3×3, 16 2×2 3×3, 32 2×2 3×3, 32 1 1
    输出 64×32×1 32×16×1 32×16×16 16×8×16 16×8×32 8×4×32 8×4×32 1×1×1 1
    下载: 导出CSV

    表  2  3组滚动轴承全寿命试验数据集相关参数

    Table  2.   Related parameters of three groups of rolling bearing whole lifetime test datasets

    数据集 轴承型号 转速/
    (r/min)
    载荷/kN 采样率/
    kHz
    采样周期/
    min
    样本
    长度
    试验
    时长/h
    故障类型
    IMS ZA 2115 2000 26.67 20 10 20480 163 外圈剥落
    IDES HRB 6206 11500 6.25 32 6 65536 30 内圈剥落
    XJTU-SY UER 204 2250 11 25.6 1 32768 8.9 保持架裂断
    下载: 导出CSV

    表  3  轴承主要参数

    Table  3.   Main parameters of bearings

    轴承型号 滚珠直径/mm 节径/mm 滚珠数 接触角/(°)
    ZA 2115 8.4 71.5 16 15.17
    HRB 6206 9.5 46 9 0
    UER 204 7.9 34.6 8 0
    下载: 导出CSV

    表  4  基于拉依达准则的滚动轴承退化与失效时刻和阈值确定

    Table  4.   Determination of degradation and failure time and threshold of rolling bearings based on Pauta criterion

    方法 ZA 2115全寿命
    (984时刻)
    HRB 6206全寿命
    (300时刻)
    UER 204全寿命
    (533时刻)
    退化开始 失效开始 退化开始 失效开始 退化开始 失效开始
    有效值 阈值 0.1061 0.1643 2.5012 4.1450 1.5452 2.9672
    时刻 649 703 251 274 341 372
    CNN特征值 阈值 0.7599 1.2234 0.6762 1.0962 0.7714 1.1806
    时刻 675 703 249 279 194 315
    Wavelet-CNN
    特征值(d1
    阈值 0.5981 1.0272 0.6420 1.0877 0.6700 0.9946
    时刻 547 702 212 275 121 135
    下载: 导出CSV

    表  5  3组滚动轴承数据基于粒子滤波RUL预测结果误差对比

    Table  5.   Error comparison of RUL prediction results of three groups of rolling bearing data based on particle filter

    数据集 特征类型 拟合曲线的
    RMSE
    预测结果的
    归一化RMSE
    ZA 2115
    (984点)
    有效值 0.0252 0.2468
    CNN特征值 0.0177 0.1483
    Wavelet-CNN
    特征值
    0.0143 0.1314
    HRB 6206
    (300点)
    有效值 0.1088 0.2668
    CNN特征值 0.0280 0.1085
    Wavelet-CNN
    特征值
    0.0629 0.1321
    UER 204
    (533点)
    有效值 0.2135 0.4118
    CNN特征值 0.1466 0.1620
    Wavelet-CNN
    特征值
    0.0863 0.1335
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-26
  • 网络出版日期:  2024-02-20

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